A rigorous framework for detecting SARS-CoV-2 spike protein mutational ensemble from genomic and structural features

Biological factors associated with long COVID and comparative analysis of SARS-CoV-2 spike protein variants: a retrospective study in Thailand

Background

Post-acute COVID-19 syndrome (long COVID) refers to the persistence of COVID-19 symptoms or exceptional symptoms following recovery. Even without conferring fatality, it represents a significant global public health burden. Despite many reports on long COVID, the prevalence and data on associated biological factors remain unclear and limited. This research aimed to determine the prevalence of long COVID during the two distinct epidemic periods in Thailand, due to the Delta and Omicron variants of SARS-CoV-2, and to investigate the biological factors associated with long COVID. In addition, the spike protein amino acid sequences of the Delta and Omicron variants were compared to determine the frequency of mutations and their potential biological implications.

Methods

A retrospective cross-sectional study was established to recruit confirmed COVID-19 participants at Maharat Nakhon Ratchasima Hospital who had recovered for at least three months and were infected between June 2021 and August 2022. The demographic data and long COVID experience were collected via telephone interview. The biological factors were analyzed through binary logistic regression. The datasets of the SARS-CoV-2 spike protein amino acid sequence of the Delta and Omicron variants in Thailand were retrieved from GIDSAID to determine mutation frequencies and to identify possible roles of the mutations based on published data.

Results

Data was collected from a total of 247 participants comprising 106 and 141 participants of the Delta and Omicron epidemic periods, respectively. Apart from the COVID-19 severity and health status, the baseline participant data of the two time periods were remarkably similar. The prevalence of long COVID observed in the Omicron period was higher than in the Delta period (74.5% vs. 66.0%). The biological factors associated with long COVID were epidemic variant, age, treatment with symptomatic medicines, and vaccination status. When the spike protein sequence data of the two variants were compared, it was observed that the Omicron variant exhibited a greater quantity of amino acid changes in its receptor-binding domain (RBD) and receptor-binding motif (RBM). The critical changes of the Omicron variant within these regions had a significant function in enhancing virus transmissibility and host immune response resistance.

Conclusion

This study revealed informative data associated with long COVID in Thailand. More attention should be given to long COVID caused by unique virus variants and other biological factors to prepare a healthcare management strategy for COVID-19 patients after recovery.

Unlocking the puzzle: non-defining mutations in SARS-CoV-2 proteome may affect vaccine effectiveness

Introduction

SARS-CoV-2 variants are defined by specific genome-wide mutations compared to the Wuhan genome. However, non-clade-defining mutations may also impact protein structure and function, potentially leading to reduced vaccine effectiveness. Our objective is to identify mutations across the entire viral genome rather than focus on individual mutations that may be associated with vaccine failure and to examine the physicochemical properties of the resulting amino acid changes.

Materials and methods

Whole-genome consensus sequences of SARS-CoV-2 from COVID-19 patients were retrieved from the GISAID database. Analysis focused on Dataset_1 (7,154 genomes from Italy) and Dataset_2 (8,819 sequences from Spain). Bioinformatic tools identified amino acid changes resulting from codon mutations with frequencies of 10% or higher, and sequences were organized into sets based on identical amino acid combinations.

Results

Non-defining mutations in SARS-CoV-2 genomes belonging to clades 21 L (Omicron), 22B/22E (Omicron), 22F/23A (Omicron) and 21J (Delta) were associated with vaccine failure. Four sets of sequences from Dataset_1 were significantly linked to low vaccine coverage: one from clade 21L with mutations L3201F (ORF1a), A27- (S) and G30- (N); two sets shared by clades 22B and 22E with changes A27- (S), I68- (S), R346T (S) and G30- (N); and one set shared by clades 22F and 23A containing changes A27- (S), F486P (S) and G30- (N). Booster doses showed a slight improvement in protection against Omicron clades. Regarding 21J (Delta) two sets of sequences from Dataset_2 exhibited the combination of non-clade mutations P2046L (ORF1a), P2287S (ORF1a), L829I (ORF1b), T95I (S), Y145H (S), R158- (S) and Q9L (N), that was associated with vaccine failure.

Discussion

Vaccine coverage associations appear to be influenced by the mutations harbored by marketed vaccines. An analysis of the physicochemical properties of amino acid revealed that primarily hydrophobic and polar amino acid substitutions occurred. Our results suggest that non-defining mutations across the proteome of SARS-CoV-2 variants could affect the extent of protection of the COVID-19 vaccine. In addition, alteration of the physicochemical characteristics of viral amino acids could potentially disrupt protein structure or function or both.

PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles

Due to the complex nature of noncovalent interactions and their long-range effects, analyzing protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) provide a convenient formalism to study protein structures in relation to essential properties such as key residues for structural stability, allosteric communication, and the effects of modifications of the protein. PSNs can be defined according to very different principles, and the available tools have limitations in input formats, supported models, and version control. Other outstanding problems are related to the definition of network cutoffs and the assessment of the stability of the network properties. The protein science community could benefit from a common framework to carry out these analyses and make them easier to reproduce, reuse, and evaluate. We here provide two open-source software packages, PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a reproducible and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and incorporates different network models with the possibility of integrating them into a macronetwork and performing various downstream analyses, including hubs, connected components, and several other centrality measures, and visualizes the networks or further analyzes them thanks to compatibility with Cytoscape.PyInKnife2 that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. We foresee that the modular structure of the code and the supported version control system will promote the transition to a community-driven effort, boost reproducibility, and establish common protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities and maintenance, assistance, and training of new contributors.

Temporal dynamics of SARS-CoV-2 genome and detection of variants of concern in wastewater influent from two metropolitan areas in Arkansas

Although SARS-CoV-2 can cause severe illness and death, a percentage of the infected population is asymptomatic. This, along with other factors, such as insufficient diagnostic testing and underreporting due to self-testing, contributes to the silent transmission of SARS-CoV-2 and highlights the importance of implementing additional surveillance tools. The fecal shedding of the virus from infected individuals enables its detection in community wastewater, and this has become a valuable public health tool worldwide as it allows the monitoring of the disease on a populational scale. Here, we monitored the presence of SARS-CoV-2 and its dynamic genomic changes in wastewater sampled from two metropolitan areas in Arkansas during major surges of COVID-19 cases and assessed how the viral titers in these samples related to the clinical case counts between late April 2020 and January 2022. The levels of SARS-CoV-2 RNA were quantified by reverse-transcription quantitative polymerase chain reaction (RT-qPCR) using a set of TaqMan assays targeting three different viral genes (encoding ORF1ab polyprotein, surface glycoprotein, and nucleocapsid phosphoprotein). An allele-specific RT-qPCR approach was used to screen the samples for SARS-CoV-2 mutations. The identity and genetic diversity of the virus were further investigated through amplicon-based RNA sequencing, and SARS-CoV-2 variants of concern were detected in wastewater samples throughout the duration of this study. Our data show how changes in the virus genome can affect the sensitivity of specific RT-qPCR assays used in COVID-19 testing with the surge of new variants. A significant association was observed between viral titers in wastewater and recorded number of COVID-19 cases in the areas studied, except when assays failed to detect targets due to the presence of particular variants. These findings support the use of wastewater surveillance as a reliable complementary tool for monitoring SARS-CoV-2 and its genetic variants at the community level.

SARS-CoV-2 from COVID-19 Patients in the Republic of Moldova: Whole-Genome Sequencing Results

Since the onset of the COVID-19 pandemic, no viral genome sequences of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have been documented from the Republic of Moldova, a developing country geographically located in Eastern Europe between Romania and Ukraine. Here, we report the analysis of 96 SARS-CoV-2 sequences from Delta and Omicron variants of the SARS-CoV-2 cases in the Republic of Moldova obtained between August and November 2021 and between January and May 2022. Comparison to global viral sequences showed that among the Delta variant of the SARS-CoV-2, AY.122 (n = 25), followed by AY.4.2.3 (n = 6), AY.4 (n = 5), AY.43 (n = 3), AY.98.1 (n = 3), B.1.617.2 (n = 1), AY.125 (n = 1), AY.54 (n = 1), AY.9 (n = 1), AY.126 (n = 1), and AY.33 (n = 1) were the most frequently found lineages. Furthermore, 10 lineages of the Omicron variant, namely, BA.2 (n = 14), followed by BA.2.9 (n = 10), BA.1 (n = 5), BA.1.1 (n = 5), BA.1.18 (n = 4), BA.1.15.1 (n = 3), BA.1.17.2 (n = 2), BA.1.17 (n = 2), BA.1.15 (n = 1), and BA.2.1 (n = 1) were detected. In addition, we also identified the impact of the military crisis between Russia and Ukraine, when the COVID-19 epidemiological rules collapsed, on the distribution of Delta and Omicron variants in the Republic of Moldova. Additional studies are warranted to characterize further the impact of the war between Russia and Ukraine on the genomic epidemiology of the SARS-CoV-2 in the Republic of Moldova and Eastern Europe.

Glucocorticoids Bind to SARS-CoV-2 S1 at Multiple Sites Causing Cooperative Inhibition of SARS-CoV-2 S1 Interaction With ACE2

Dexamethasone may reduce mortality in COVID-19 patients. Whether dexamethasone or endogenous glucocorticoids, such as cortisol, biochemically interact with SARS-CoV-2 spike 1 protein (S1), or its cellular receptor ACE2, is unknown. Using molecular dynamics (MD) simulations and binding energy calculations, we identified 162 druggable pockets in various conformational states of S1 and all possible binding pockets for cortisol and dexamethasone. Through biochemical binding studies, we confirmed that cortisol and dexamethasone bind to S1. Limited proteolysis and mass spectrometry analyses validated several MD identified binding pockets for cortisol and dexamethasone on S1. Interaction assays indicated that cortisol and dexamethasone separately and cooperatively disrupt S1 interaction with ACE2, through direct binding to S1, without affecting ACE2 catalytic activity. Cortisol disrupted the binding of the mutant S1 Beta variant (E484K, K417N, N501Y) to ACE2. Delta and Omicron variants are mutated in or near identified cortisol-binding pockets in S1, which may affect cortisol binding to them. In the presence of cortisol, we find increased inhibition of S1 binding to ACE2 by an anti-SARS-CoV-2 S1 human chimeric monoclonal antibody against the receptor binding domain. Whether glucocorticoid/S1 direct interaction is an innate defence mechanism that may have contributed to mild or asymptomatic SARS-CoV-2 infection deserves further investigation.

A Secure Artificial Intelligence-Enabled Critical Sars Crisis Management Using Random Sigmoidal Artificial Neural Networks

Since December 2019, the pandemic COVID-19 has been connected to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early identification and diagnosis are essential goals for health practitioners because early symptoms correlate with those of other common illnesses including the common cold and flu. RT-PCR is frequently used to identify SARS-CoV-2 viral infection. Although this procedure can take up to 2 days to complete and sequential monitoring may be essential to figure out the potential of false-negative findings, RT-PCR test kits are apparently in low availability, highlighting the urgent need for more efficient methods of diagnosing COVID-19 patients. Artificial intelligence (AI)-based healthcare models are more effective at diagnosing and controlling large groups of people. Hence, this paper proposes a novel AI-enabled SARS detection framework. Here, the input CT images are collected and preprocessed using a block-matching filter and histogram equalization (HE). Segmentation is performed using Compact Entropy Rate Superpixel (CERS) technique. Features of segmented output are extracted using Histogram of Gradient (HOG). Feature selection is done using Principal Component Analysis (PCA). The suggested Random Sigmoidal Artificial Neural Networks (RS-ANN) based classification approach effectively diagnoses the existence of the disease. The performance of the suggested Artificial intelligence model is analyzed and related to existing approaches. The suggested AI system may help identify COVID-19 patients more quickly than conventional approaches.

Computer Simulations and Network-Based Profiling of Binding and Allosteric Interactions of SARS-CoV-2 Spike Variant Complexes and the Host Receptor: Dissecting the Mechanistic Effects of the Delta and Omicron Mutations

In this study, we combine all-atom MD simulations and comprehensive mutational scanning of S-RBD complexes with the angiotensin-converting enzyme 2 (ACE2) host receptor in the native form as well as the S-RBD Delta and Omicron variants to (a) examine the differences in the dynamic signatures of the S-RBD complexes and (b) identify the critical binding hotspots and sensitivity of the mutational positions. We also examined the differences in allosteric interactions and communications in the S-RBD complexes for the Delta and Omicron variants. Through the perturbation-based scanning of the allosteric propensities of the SARS-CoV-2 S-RBD residues and dynamics-based network centrality and community analyses, we characterize the global mediating centers in the complexes and the nature of local stabilizing communities. We show that a constellation of mutational sites (G496S, Q498R, N501Y and Y505H) correspond to key binding energy hotspots and also contribute decisively to the key interfacial communities that mediate allosteric communications between S-RBD and ACE2. These Omicron mutations are responsible for both favorable local binding interactions and long-range allosteric interactions, providing key functional centers that mediate the high transmissibility of the virus. At the same time, our results show that other mutational sites could provide a "flexible shield" surrounding the stable community network, thereby allowing the Omicron virus to modulate immune evasion at different epitopes, while protecting the integrity of binding and allosteric interactions in the RBD-ACE2 complexes. This study suggests that the SARS-CoV-2 S protein may exploit the plasticity of the RBD to generate escape mutants, while engaging a small group of functional hotspots to mediate efficient local binding interactions and long-range allosteric communications with ACE2.

Landscape-Based Protein Stability Analysis and Network Modeling of Multiple Conformational States of the SARS-CoV-2 Spike D614G Mutant: Conformational Plasticity and Frustration-Induced Allostery as Energetic Drivers of Highly Transmissible Spike Variants

The structural and functional studies of the SARS-CoV-2 spike protein variants revealed an important role of the D614G mutation that is shared across many variants of concern (VOCs), suggesting the effect of this mutation on the enhanced virus infectivity and transmissibility. The recent structural and biophysical studies provided important evidence about multiple conformational substates of the D614G spike protein. The development of a plausible mechanistic model that can explain the experimental observations from a more unified thermodynamic perspective is an important objective of the current work. In this study, we employed efficient and accurate coarse-grained simulations of multiple structural substates of the D614G spike trimers together with the ensemble-based mutational frustration analysis to characterize the dynamics signatures of the conformational landscapes. By combining the local frustration profiling of the conformational states with residue-based mutational scanning of protein stability and network analysis of allosteric interactions and communications, we determine the patterns of mutational sensitivity in the functional regions and sites of variants. We found that the D614G mutation may induce a considerable conformational adaptability of the open states in the SARS-CoV-2 spike protein without compromising the folding stability and integrity of the spike protein. The results suggest that the D614G mutant may employ a hinge-shift mechanism in which the dynamic couplings between the site of mutation and the interprotomer hinge modulate the interdomain interactions, global mobility change, and the increased stability of the open form. This study proposes that mutation-induced modulation of the conformational flexibility and energetic frustration at the interprotomer interfaces may serve as an efficient mechanism for allosteric regulation of the SARS-CoV-2 spike proteins.

Structural and Computational Studies of the SARS-CoV-2 Spike Protein Binding Mechanisms with Nanobodies: From Structure and Dynamics to Avidity-Driven Nanobody Engineering

Nanobodies provide important advantages over traditional antibodies, including their smaller size and robust biochemical properties such as high thermal stability, high solubility, and the ability to be bioengineered into novel multivalent, multi-specific, and high-affinity molecules, making them a class of emerging powerful therapies against SARS-CoV-2. Recent research efforts on the design, protein engineering, and structure-functional characterization of nanobodies and their binding with SARS-CoV-2 S proteins reflected a growing realization that nanobody combinations can exploit distinct binding epitopes and leverage the intrinsic plasticity of the conformational landscape for the SARS-CoV-2 S protein to produce efficient neutralizing and mutation resistant characteristics. Structural and computational studies have also been instrumental in quantifying the structure, dynamics, and energetics of the SARS-CoV-2 spike protein binding with nanobodies. In this review, a comprehensive analysis of the current structural, biophysical, and computational biology investigations of SARS-CoV-2 S proteins and their complexes with distinct classes of nanobodies targeting different binding sites is presented. The analysis of computational studies is supplemented by an in-depth examination of mutational scanning simulations and identification of binding energy hotspots for distinct nanobody classes. The review is focused on the analysis of mechanisms underlying synergistic binding of multivalent nanobodies that can be superior to single nanobodies and conventional nanobody cocktails in combating escape mutations by effectively leveraging binding avidity and allosteric cooperativity. We discuss how structural insights and protein engineering approaches together with computational biology tools can aid in the rational design of synergistic combinations that exhibit superior binding and neutralization characteristics owing to avidity-mediated mechanisms.

Allosteric Determinants of the SARS-CoV-2 Spike Protein Binding with Nanobodies: Examining Mechanisms of Mutational Escape and Sensitivity of the Omicron Variant

Structural and biochemical studies have recently revealed a range of rationally engineered nanobodies with efficient neutralizing capacity against the SARS-CoV-2 virus and resilience against mutational escape. In this study, we performed a comprehensive computational analysis of the SARS-CoV-2 spike trimer complexes with single nanobodies Nb6, VHH E, and complex with VHH E/VHH V nanobody combination. We combined coarse-grained and all-atom molecular simulations and collective dynamics analysis with binding free energy scanning, perturbation-response scanning, and network centrality analysis to examine mechanisms of nanobody-induced allosteric modulation and cooperativity in the SARS-CoV-2 spike trimer complexes with these nanobodies. By quantifying energetic and allosteric determinants of the SARS-CoV-2 spike protein binding with nanobodies, we also examined nanobody-induced modulation of escaping mutations and the effect of the Omicron variant on nanobody binding. The mutational scanning analysis supported the notion that E484A mutation can have a significant detrimental effect on nanobody binding and result in Omicron-induced escape from nanobody neutralization. Our findings showed that SARS-CoV-2 spike protein might exploit the plasticity of specific allosteric hotspots to generate escape mutants that alter response to binding without compromising activity. The network analysis supported these findings showing that VHH E/VHH V nanobody binding can induce long-range couplings between the cryptic binding epitope and ACE2-binding site through a broader ensemble of communication paths that is less dependent on specific mediating centers and therefore may be less sensitive to mutational perturbations of functional residues. The results suggest that binding affinity and long-range communications of the SARS-CoV-2 complexes with nanobodies can be determined by structurally stable regulatory centers and conformationally adaptable hotspots that are allosterically coupled and collectively control resilience to mutational escape.

Conformational Flexibility and Local Frustration in the Functional States of the SARS-CoV-2 Spike B.1.1.7 and B.1.351 Variants: Mutation-Induced Allosteric Modulation Mechanism of Functional Dynamics and Protein Stability

Structural and functional studies of the SARS-CoV-2 spike proteins have recently determined distinct functional states of the B.1.1.7 and B.1.351 spike variants, providing a molecular framework for understanding the mechanisms that link the effect of mutations with the enhanced virus infectivity and transmissibility. A detailed dynamic and energetic analysis of these variants was undertaken in the present work to quantify the effects of different mutations on functional conformational changes and stability of the SARS-CoV-2 spike protein. We employed the efficient and accurate coarse-grained (CG) simulations of multiple functional states of the D614G mutant, B.1.1.7 and B.1.351 spike variants to characterize conformational dynamics of the SARS-CoV-2 spike proteins and identify dynamic signatures of the functional regions that regulate transitions between the closed and open forms. By combining molecular simulations with full atomistic reconstruction of the trajectories and the ensemble-based mutational frustration analysis, we characterized how the intrinsic flexibility of specific spike regions can control functional conformational changes required for binding with the host-cell receptor. Using the residue-based mutational scanning of protein stability, we determined protein stability hotspots and identified potential energetic drivers favoring the receptor-accessible open spike states for the B.1.1.7 and B.1.351 spike variants. The results suggested that modulation of the energetic frustration at the inter-protomer interfaces can serve as a mechanism for allosteric couplings between mutational sites and the inter-protomer hinges of functional motions. The proposed mechanism of mutation-induced energetic frustration may result in greater adaptability and the emergence of multiple conformational states in the open form. This study suggested that SARS-CoV-2 B.1.1.7 and B.1.351 variants may leverage the intrinsic plasticity of functional regions in the spike protein for mutation-induced modulation of protein dynamics and allosteric regulation to control binding with the host cell receptor.